This paper presents a novel approach to active learning with image data, combining recent advances in Bayesian deep learning. The authors develop an active learning framework for high-dimensional data, which has been a challenging task with limited existing literature. They demonstrate their approach using Bayesian convolutional neural networks (BCNNs), achieving significant improvements over existing active learning methods. The framework is tested on the MNIST dataset and on skin cancer diagnosis from lesion images (ISIC2016 task). The paper highlights the challenges of active learning in high-dimensional data, where traditional methods often rely on model uncertainty, which is difficult to represent in deep learning. The authors propose using Bayesian approaches to model uncertainty, enabling the active learning framework to select informative data points for labeling. They evaluate several acquisition functions, including BALD, Variation Ratios, Max Entropy, and Mean STD, and find that BALD performs best in terms of data efficiency and accuracy. The authors compare their approach to existing techniques, including kernel-based methods and semi-supervised learning. They show that their Bayesian CNN-based active learning approach outperforms these methods on the MNIST dataset, achieving lower test error with fewer labeled images. They also demonstrate the effectiveness of their approach in a real-world application, diagnosing melanoma from lesion images using a fine-tuned VGG16 CNN model. The paper emphasizes the importance of model uncertainty in active learning, particularly in high-dimensional data. They show that Bayesian models, which capture both aleatoric and epistemic uncertainty, outperform deterministic models in terms of accuracy and data efficiency. The authors also discuss the computational challenges of their approach, including the need for prolonged training times, and suggest that future research could focus on reducing these costs without sacrificing performance.This paper presents a novel approach to active learning with image data, combining recent advances in Bayesian deep learning. The authors develop an active learning framework for high-dimensional data, which has been a challenging task with limited existing literature. They demonstrate their approach using Bayesian convolutional neural networks (BCNNs), achieving significant improvements over existing active learning methods. The framework is tested on the MNIST dataset and on skin cancer diagnosis from lesion images (ISIC2016 task). The paper highlights the challenges of active learning in high-dimensional data, where traditional methods often rely on model uncertainty, which is difficult to represent in deep learning. The authors propose using Bayesian approaches to model uncertainty, enabling the active learning framework to select informative data points for labeling. They evaluate several acquisition functions, including BALD, Variation Ratios, Max Entropy, and Mean STD, and find that BALD performs best in terms of data efficiency and accuracy. The authors compare their approach to existing techniques, including kernel-based methods and semi-supervised learning. They show that their Bayesian CNN-based active learning approach outperforms these methods on the MNIST dataset, achieving lower test error with fewer labeled images. They also demonstrate the effectiveness of their approach in a real-world application, diagnosing melanoma from lesion images using a fine-tuned VGG16 CNN model. The paper emphasizes the importance of model uncertainty in active learning, particularly in high-dimensional data. They show that Bayesian models, which capture both aleatoric and epistemic uncertainty, outperform deterministic models in terms of accuracy and data efficiency. The authors also discuss the computational challenges of their approach, including the need for prolonged training times, and suggest that future research could focus on reducing these costs without sacrificing performance.